In wireless communication systems, a received signal experiences significant power fluctuations due to fading. Signal fading is caused by multipath propagation and Doppler frequency shift. Multiple scatterers cause interference between reflected transmitter signal components. As a mobile receiver moves through the interference pattern set up by the multiple scatterers, it experiences a specific fading pattern, which is unique to the mobile path and the scattering environment, and is usually time-varying. The superposition of scattered component waves can lead to constructive and destructive interference, which create fading peaks and deep fades, respectively.
Channel fading prediction can be used to improve the performance of communication systems. Having estimates of future channel characteristics can facilitate and enhance the performance of many tasks of the receiver and the transmitter, such as channel equalization, data symbol decoding, antenna beamforming, and adaptive modulation.
To predict a process, a time evolution model of the process is required. Channel fading can be modeled using linear models, such as auto-regressive moving-average (ARMA) models. Such linear models are easy to use, and have low complexity. However, the fading process is highly nonlinear, and can not be exactly modeled with a reasonable linear filter. Therefore, for short-range applications, an approximate low-order auto-regressive (AR) model has been used to capture most of the fading dynamics. However, linear models do not perform well for long-range predictions, and exhibit poor performance for high mobility channels, as they are solely dependent on the correlation parameters of the fading process.
The use of deterministic sum-sinusoidal models to estimate channel fading has also been proposed. These models rely on complex estimations of amplitude, phase and Doppler shift frequencies. Thus, the shorter the estimation window, the higher the complexity, and the longer the estimation window, the higher the prediction errors. As a result, such models tend to be highly complex, or inaccurate.
It is, therefore, desirable to provide a low-complexity channel prediction system and method effective for long-range predictions.